Social dimensions impact individual sleep quantity and quality

While sleep positively impacts well-being, health, and productivity, the effects of societal factors on sleep remain underexplored. Here we analyze the sleep of 30,082 individuals across 11 countries using 52 million activity records from wearable devices. Our data are consistent with past studies of gender and age-associated sleep characteristics. However, our analysis of wearable device data uncovers differences in recorded vs. self-reported bedtime and sleep duration. The dataset allowed us to study how country-specific metrics such as GDP and cultural indices relate to sleep in groups and individuals. Our analysis indicates that diverse sleep metrics can be represented by two dimensions: sleep quantity and quality. We find that 55% of the variation in sleep quality, and 63% in sleep quantity, are explained by societal factors. Within a societal boundary, individual sleep experience was modified by factors like exercise. Increased exercise or daily steps were associated with better sleep quality (for example, faster sleep onset and less time awake in bed), especially in countries like the U.S. and Finland. Understanding how social norms relate to sleep will help create strategies and policies that enhance the positive impacts of sleep on health, such as productivity and well-being.

Recent years have seen breakthroughs in research into the biological, cellular, and biochemical mechanisms underlying sleep. The circadian clock modulates sleep based on the timing and levels of natural and artificial light exposure 1 . However, multiple factors influence sleep, including environmental (e.g., sunset and sunrise times), social (e.g., cultural norms), and individual attributes (e.g., age and gender). The human sleep project advocated equipping people with a range of mobile sleep-tracking devices to study their sleep patterns in realtime and on a large scale, referring to the impact of social factors as the next frontier in sleep research 2 . Studying the interplay between sleep and societal factors necessitates large-scale datasets that bridge multiple societies. Recent app-based surveys and sensing technologies have expanded sleep analysis to the population level 3,4 and even to a global scale 5,6 . However, most previous sleep studies focused on small-scale data 7 .
Past research indicates that different methods lead to discrepancies with actual sleep levels. Indeed, selfreports, commercial accelerometers, actigraphy, and even the gold standard for measuring sleep, polysomnography, have been shown to overestimate or underestimate sleep 6,[8][9][10] . Recent years have seen advances in commercial devices that record individual activity. A study conducted in 2017 found that although wearable devices were less effective in tracking sleep patterns in individuals with sleep disorders, they were still comparable to the gold standard methods used for monitoring normal sleep 11 . A large dataset from Fitbit was analyzed in another study to examine the variations in sleep behaviors among different social groups 12 . The study found significant differences in sleep patterns based on gender and age among people living in Oceania and East Asia. These findings prompted questions about the potential factors, such as genetic or adaptive mechanisms, that contribute to these differences. With these issues in mind, we assessed wearables as a source of data for examining the influence of culture on these sleep patterns, leveraging their wide adoption and ability to track various aspects of human physiology.
Here we analyzed sleep and physical activity data from 30,082 individuals who wore the same wearable device brand. Data from individuals representing 19 cities across three continents were examined. The data allowed us to study sleep across multiple societal boundaries at an unprecedented scale. The 52 million activity records and user demographic information recapitulated known influences of age and gender on sleep. However, bedtimes measured using wearables appeared substantially delayed compared to self-reports, suggesting a systematic reduction in actual sleep duration. We found that social effects explain up to 63% of variations in measurements www.nature.com/scientificreports/ of sleep quantity measures (e.g., sleep duration), and 55% of the variation in sleep quality measures (e.g., sleep efficiency). When we controlled for country effects, our data show that enhanced or poor sleep experiences are explained by controllable factors such as exercise. The effect size, or the balance between societal and individual influence, varied by country. This interplay between social constructs and individual behavioral efforts has not previously been recorded on the scale examined here. Our analysis provides a quantitative basis for designing guidelines and policy interventions that center on sleep for social and welfare programs.

Results
The data analyzed came from 11 different countries (Fig. 1A) and were collected between 2014 and 2017 in 19 cities where the studied wearable device was prevalent. The smallest city contained 495 users, and the largest city contained 8924 users. Sleep was inferred by the sensing technologies in wearable devices and the associated algorithms, which indicated a global median of 12:01 AM bedtime and 7:42 AM wake time, respectively (Fig. 1B).
The data contained user demographic information and both daytime and nighttime activity. The user base had a slightly higher presence of males (55%) and older adults (i.e., the median age was 42 years). The median BMI and number of steps per day recorded were 25.4 and 6,951, respectively (Fig. 1C). The day-of-week pattern indicated that, in general, users were most active on Wednesdays (Fig. 1D). This finding is consistent with other observations of higher activity (e.g., trading volume, physical activity) in the middle of the week [13][14][15] . For all groups younger than 70, bedtimes and wake times were generally much later in females than males (Fig. 1E). Additionally, females had significantly shorter sleep durations than males, particularly for individuals aged 30-40 (refer to  Testing the presence of country effects on sleep quantity, we also examined how sleep quantity varied according to standard social constructs. In addition to log-GDP, we incorporated Hofstede's cultural dimensions and considered well-established indices, such as individualism (IDV) and uncertainty avoidance index (UAI), and found meaningful correlations with sleep measures (see Materials and Methods and Fig S2 in Supplementary Material) confirming significant social influence on sleep. In the individual-level assessment, as presented in Table S7 of the Supplementary Material, the city of residence could explain only a marginal proportion of the Individual activity can improve sleep within societal boundaries. In order to uncover individual influences on sleep, we divided the individuals residing in each city into the top one-third and the bottom onethird based on their activity levels (measured by daily steps, as described in Materials and Methods). We then compared their composite sleep dimensions using propensity score matching. We found that individuals who exercise have better sleep efficiency, which means they spend a larger portion of their time in bed actually sleeping. Their total sleep duration is also shorter than that of non-exercising individuals by a small margin. As a   Fig. 3 shows that exercising individuals exhibit negative sleep quantities compared to less active individuals in the same city, but they enjoy better sleep quality, yet this effect varied depending on the residing country.
To interpret these findings, we ran regression models to find the relationship between sleep and a unit increase in physical activity (refer to Table S8 in Supplementary Material). For instance, among U.S. residents who sleep for 8 h, an additional 1000 steps per day, on average, led to an increase in active sleep duration by 1.28 minutes. However, the total duration spent in bed (sleeping or not) decreased by 2.47 min, perhaps to accommodate the extra time devoted to physical activity. The Supplementary Material contains additional information on relationships between sleep and physical activity, age, and gender relative to cultural dimensions.

Discussion
In this study, we utilized data from a single brand of wearable devices to assess sleep patterns in a large-scale sample of 30,082 individuals across 11 countries. These findings are discussed further below.
Data from wearables concur with previously known sleep patterns but reveal substantially delayed bedtimes and shorter sleep duration. While our data confirmed previously studied relationships between bedtime, wake time, and sleep duration ( Fig. 2A), the scale and international nature of the data also revealed new insights into sleep patterns. Sleep duration measured using wearable data is more strongly determined by bedtime than wake time, suggesting the influence of social obligations at night time, as seen in survey-based data in previous studies 5 . Multiple epidemiological and cohort studies have shown that increasing age, in general, is associated with shorter sleep duration and earlier wake times 16 . A reduction in total sleep duration of 0.5 min per year of age increase was seen in the large multicenter community Sleep Heart Health research (n = 2113) 17 . Another study revealed that a short sleep duration prevails in teenagers and those aged > 65 years 7 . Our data also show that age has a nonlinear relationship with sleep timing and duration (Fig. S1). The consistency of findings relative to other methodologies indicates that wearable data serve as a valuable comparative tool for sleep studies, as first demonstrated in 11 . Moreover, the wearable data also provide objective, quantitative data measurements.
Indeed, the wearable data indicated several visible differences in sleep patterns compared to studies based on self-reporting. For example, previous research found that women sleep more than men and generally have earlier bedtimes and later wake times until the age of 60 5,6 . Meanwhile, our data shows the same patterns for bedtimes, but not for wake times, in female and male cohorts (Fig. 1E). Previous wearable studies also showed that women tend to self-report shorter and less efficient sleep in surveys compared to actigraphy measurements 6 . Our wearable data also reported later bedtimes and wake times compared to survey-based studies in all countries that were examined using both approaches (see Fig. 4). The highest quantity of sleep among the 11 countries, according to wearable data, was found in Finland, where the median daily sleep time was 8 h. The reported sleep time in all other nations was 16-69 min shorter than it was in Finland (Table S1). Data from Japan showed the . Across all target countries, the EATE values were negative for RC1 (indicating less sleep quantity) and positive for RC2 (indicating better sleep quality) for the active group (top 30% of users in terms of average daily steps) compared to the non-active group (bottom 30% of users). This holds across countries but is prominent in, for example, the US and Finland as they are at the bottom of the quantity graph and the top of the quality graph. www.nature.com/scientificreports/ lowest median sleep duration, 6 h and 51 min, and the biggest difference between wearable-recorded and selfreported sleep measurements. Focusing on country-specific variations in Fig. 2B, we identified that three sleep measures substantially correlate with log-GDP for the studied countries, a relationship that has not been extensively examined in previous studies. Specifically, bedtime was found to be delayed in higher-income countries, yet wake time was affected to a lesser degree. Higher-income countries also showed shorter average sleep durations. The relationship between GDP and sleep measures observed in our study could be driven by various mechanisms. One possibility is that higher-income countries may have more demanding work schedules or longer working hours, leading to delayed bedtimes and shorter sleep durations 20 . Another potential factor is that higher-income countries may have greater access to technology and entertainment, resulting in later bedtimes and reduced sleep durations due to increased exposure to screens and devices 21 .
According to the analysis, a handful of important social factors such as GDP, Individualism, and Uncertainty Avoidance are able to account for 63% of the differences in sleep quantity and 55% of the differences in sleep quality across different locations (refer to Table 1). This finding suggests that the amount and quality of sleep may be linked to the specific societal and cultural characteristics of the region where individuals reside. For example, considering the baseline models, living in higher GDP regions was associated with shorter sleep duration (p < 0.01) and lower sleep efficiency (p < 0.01). In contrast, considering the final StepAIC model, countries with higher individualism Index (IDV), which indicates the degree to which a culture reinforces individual achievement and relationships, had longer sleep duration (p < 0:01), which may be explained by an earlier bedtime schedule in such societies. Countries with high collectivism scores, i.e., low IDV values, may have stronger social obligations at night, potentially leading to shorter sleep duration. For instance, the cultural emphasis on social harmony and group obligations in collectivistic cultures may create stronger expectations for social engagement or activities at night, which can in turn impact individuals' sleep behaviors. Individuals who work long hours or have rigid work schedules that extend into the evening may also have fewer opportunities for restful sleep. Spain and Japan were the two countries in the studied data that had the highest collectivism scores and the most delayed bedtime schedules (refer to Fig. S2 in Supplementary Material). In contrast, wake times were less divergent across the studied countries. Both high individualism (IDV) index and high uncertainty avoidance (UAI) index, which capture how regulated life is, were also associated with better sleep quality, but to a moderate degree (p < 0:1).
Previous survey-based studies have shown that bedtimes and wake times are strongly dependent on demographics such as age and gender. We confirmed the same correlations in Fig. S1. In addition, we newly explored whether these trends can be modified by individually configurable factors. One study conducted a sleep intervention experiment in a poor urban area and showed that half-hour naps at the workplace can induce highquality sleep and therefore increase work productivity by an average of 2.3% throughout the day 22 . Our analysis of individual factors also highlights, within societal boundaries, that increasing daily steps can further affect sleep (see Fig. 3). People who exercised had better sleep quality, and their total sleep duration was shorter than that of less active individuals. The effect of exercising and increased daily activity was more pronounced in some countries (e.g., the U.S., Finland) than in other countries (e.g., Austria). We also found that Japan was an exceptional case where additional daily steps did not lead to increased sleep quality. These findings suggest that the relationship between daily activity and sleep may be country-specific, and different exercise regimes may be more effective for some cultures than others. Further research could explore this possibility in more depth.
Theoretical implications. Our findings have three theoretical implications. First, we provide evidence that societal influences should be considered in modeling sleep. Previous literature showed physiological and environmental effects on sleep and suggested that individuals have unique internal time signatures 23 . Along with recent literature that discusses social influences 2,5,6 , our work offers strong evidence, supported with extensive data, that social constructs can explain the majority of variations in sleep patterns ( Table 1).
The second theoretical implication is that our results bring clarity and structure to the numerous sleep metrics available in the literature [24][25][26][27] . Upon analyzing seven metrics, we found that sleep patterns can largely be summarized using two orthogonal dimensions (83.14% of the explained variability): Principal Component Analysis (PCA), based on our data, shows that sleep metrics can be classified into those reflecting quantity (i.e., sleep duration, sleep history, and MSFsc), and those reflecting quality (i.e., sleep efficiency, the K-hour deviation). www.nature.com/scientificreports/ These metrics were chosen based on their widespread use in sleep research and their relevance to capturing important aspects of sleep patterns. The third theoretical implication comes from the limitations of existing surveys on sleep. Many studies have captured intended or desirable sleep behaviors, but very few have examined sleep in relation to participants' other life signals. This limitation can be rectified by ensuring that surveys contain questions that give insight into activities related to well-being 28,29 . Surveys can include questions about expected work shift timing, the number of holidays, access to electronic devices in the bedroom, and cultural expectations about sleep. Our results suggest a tradeoff between one's social life and sleep patterns. Societies that sacrifice a bit of sleep tend to show markers of better social life; they focus on the collective experience (Collectivism, i.e., smaller log-IDV values) by enjoying the moment and spontaneous lives (i.e., smaller Uncertainty Avoidance Index values). We also saw a poorer sleep tendency, both in terms of quantity and quality, in cities with higher GDP (i.e., larger social scales).
Practical implications. Our findings also have two practical implications. The first concerns multinational organizations. Based on our findings, international organizations may want to vary non-work activities and tailor them to local societal norms. For example, some societies emphasize individual work over sleep. This tendency could be counterbalanced by generous after-work activities that support productivity and, ultimately, well-being.
The second practical implication concerns sleep-tracking apps. These apps should consider not only "who the user is" (i.e., individual characteristics, including exercise, diet, nutrition, and quality of life) but also "where (s)he lives" (i.e., social influences and scales). For instance, there is a strong correlation between economic indicators like GDP and the quantity of artificial light present at night 30 . Since excessive exposure to artificial light may impact sleep and health, app designers may want to take these local factors into account in future iterations of their sleep devices 31 . By implementing this information, an app could recommend optimal sleep patterns for users that, for example, do not compromise an individual's social life.
Limitations and future work. This work comes with several limitations. One is measurement accuracy in sensing sleep. Although wearable devices are advancing and are widely used, the gold standard for measuring activity signals consists of less ubiquitous actigraph sensors or polysomnography 32 . Such direct measurement methods can detect variant sleep patterns. Other less invasive methods, like survey-based research, suffer from reporting biases. In fact, our data show a consistent deviation between sleep data obtained from surveys and wearable devices. Figure 4 shows average sleep schedules obtained from six different sources across 15 countries (see Table S3 for more detailed statistics), where bars indicate time frames when individuals went to bed and woke up on average. In the surveys, users reported going to bed earlier at night and waking up earlier in the morning compared to users in wearable-based studies across multiple data sources. To increase accuracy, researchers can make existing wearables 'smarter' with on-device machine learning 33 or make existing highprecision devices less invasive by miniaturizing them 34 . Furthermore, deep-learning algorithms could be used to fill in missing data points and prune possible outliers 24 .
Another limitation concerns the control variables and generalizability. While our study controlled for two common factors directly related to sleep (i.e., overall socioeconomic status of a city and median age), future studies could control for additional variables (e.g., gender or income) whenever available. Previous research emphasized the potential for interaction between, as well as the mutual influence of, genetics and culture 35 . As noted by Ong et al. 12 it is also plausible that genetic variation accounts for cultural disparities in the sleep habits of users. For example, it has been hypothesized that East Asians may have genetic variations that give them resilience to sleep deprivation 36 . By examining how genetic information affects these disparities, future research may reveal the mechanisms underlying differences in sleep patterns across ethnic groups.
Also, our findings are limited to countries with relatively higher GDP values (i.e., at least $252 billion or within the top 42 countries as of 2023) and may not be generalizable to more socio-economically deprived countries. Furthermore, tech-savvy individuals will tend to wear high-end tracking devices, leading to potential population bias in wearable-based studies. Despite this potential homogeneity in our sample, there were still stark differences in sleep quality that were explained by social constructs.

Ethical considerations. This study adhered to the General Data Protection Regulation (GDPR) in terms
of data collection, processing, and storage. The authors had no access to personally identifiable information of individuals in the studied dataset. All data were analyzed at the aggregate level (i.e., cities, countries, and groups by sleep patterns).

Materials and methods
Data collection. As consumer-grade wearables are now fully equipped with body sensors, it is possible to gather measures related to an individual's well-being (e.g., physical activity or sleep) at a large scale 24,25 . We obtained sleep readings from commercial wearable trackers worn by 30,082 unique users (55% male and 42 years old median age with a standard deviation of 12 years) between 2014 and 2017 across 19 major cities and capitals in the countries with GDP per capita rank above 34 and GDP above 42 [https:// www. world omete rs. info/ gdp/ gdp-by-count ry/], spanning three continents. The city with the lowest penetration had 495 users (median = 1043 and mean = 1588). In total, we collected 28.5 million sleep measurements. The device inferred sleep based on a combination of sensors and reported the measurements as presented in Table 2 www.nature.com/scientificreports/ that alternative, less expensive, and scalable solutions like using consumer-grade wearable devices can be a reliable way to study sleep 18,26 . The discussion of studies that compare wearable device measurement accuracy to gold standard actigraphy can be found in the Supplementary Material. In this study, sleep measures were collected using consumer-grade wrist-worn devices serviced by Nokia. All the measurements in our dataset were collected by the same version and type of wearable. These devices offer sleep-tracking functionality that continuously collects movement data through an accelerometer, aggregates it at a minute-level granularity, and uses it as input for a proprietary algorithm to estimate whether the person is sleeping. The above measurements in Table 2, logged in units of minutes, can be used to model conventional sleep metrics, including MSFsc 27 , hours overslept on free days or non-workdays 37 , the K-hour deviation 38 , and sleep efficiency 39 :

Sleep duration:
The aggregate time spent in bed, whether asleep or awake. This metric is often used as a proxy for the quantity of rest that people get at night 7,40,41 . The higher its value, the more time people sleep.
Sleep history: A seven-day average sleep duration that gives higher weights to recent days. It captures an aggregate measure of sleep behavior using the following formula, where i is the ith day in the past based on the target date. Note that sleep duration is weighted by a decaying exponential with a time constant of 7 days 42 , indicating that recent measurements have greater importance. The calculation of Sleep history is normalized such that weights sum to one, making the metric more interpretable as a weighted average of sleep duration over the past week 43 . The higher its value, the more time people slept over the past week.
Hours Overslept: Additional hours of sleep on free days compared to weekdays. Hours overslept on free days can serve as a proxy for sleep debt, as it is a cumulative effect of not getting enough sleep 37 . When people need to wake up earlier than their normal biological time and subsequently oversleep on free days to compensate for the accumulated sleep debt during the week 44,45 . The higher its value, the more people sleep on free days.
,where SD f is the average sleep duration on free days per week and SD w is the average sleep duration on workdays 4 . In the current work, we set Sunday-Thursday as work nights and Friday-Saturday as free nights according to literature 4 .

MSFsc:
This metric is used to classify chronotypes, which refer to an individual's sleep pattern preference 27 . It is calculated by determining the midpoint of sleep on free days and adjusting it based on the individual's chronotype. To calculate the MSFsc, we first calculate Hours Overslept on free days which represent the number of extra hours an individual sleeps on her free days compared to her average sleep hours on workdays, as explained above. Once Hours Overslept is calculated, we then compute the MSFsc using the following formula 4 : Here, MSF stands for the midpoint of sleep on free days, while SC represents the correction of sleep debt on workdays. The higher the value of MSFsc, the later an individual's midpoint of sleep on free days becomes. In other words, a higher MSFsc value indicates a preference for staying up later and waking up later on free days. K-hour deviation: This metric is inspired by the concept of recommended sleep duration in society and is computed as the time deviation from k hours of duration in bed. This metric captures the extent to which people deviate from the recommended hours of rest (i.e., 7 or 8). The higher its value, the greater the divergence of people's daily sleep time from the recommended sleep duration 38,46,47 . In this study, we provide the findings for the more typical k value of eight 48  www.nature.com/scientificreports/ Efficiency: The ratio of total hours slept over the total in-bed duration, as used in previous research 6,37,50 . This metric is used to represent sleep quality. Sleep efficiency can be measured by both surveys, as in Jenkins Sleep Problems Scale, or by sensors on wearable devices. The higher its value, the more quality sleep people acquire.

Social constructs.
To address the effect of culture on sleep quantity and quality, we examined how these measures varied according to popular social constructs. From Hofstede's insights portal [Hofstede's cultural dimensions: https:// www. hofst ede-insig hts. com/ produ ct/ compa re-count ries], we obtained data in four dimensions for the 19 cities examined in the current study. Since some measures were skewed, we log-transformed these values if they were not normally distributed.
• The Individualism (IDV) dimension captures the degree to which a culture reinforces individual achievement and relationships. Greater values indicate more Individualism. We also considered the Gross domestic product (GDP) as a commonly used socio-economic factor that reflects the wealth of an area.
• log-GDP: For each city, we collected yearly GDP data from the OECD regional statistics, then averaged and log-transformed it using the natural logarithm due to its skewed distribution (log-GDP: min = 11.31, max = 14.38, median = 12.65, mean = 12.74, and SD = 0.95).

Principal component analysis (PCA) for dimension reduction of sleep traits.
For each user in our dataset, we computed mean values of five sleep metrics: sleep duration, sleep history, MSFsc, K-hour deviation, and sleep efficiency. When computing these sleep features, the number of target users decreased to 23,812 as we discarded user data if data points on free days and workdays fell below a threshold: every week in our data contained at least one free day and two workdays. While the five sleep measures reflected various sleep aspects, some captured similar concepts. With the sleep records of thousands of users at hand, we could assess which fundamental dimensions captured different aspects of sleep patterns in a data-driven fashion. We did so by running a principal component analysis (PCA) to reduce the dimensionality of our sleep predictors (see Table S5, which lists the PCA loading matrix). We found that the first two principal components had eigenvalues larger than one (2.98 and 1.18, respectively) and, as depicted in Fig. S3(B), accounted for 83.14% of the total variance, which is high, considering that 60% variance is typically deemed satisfactory in social sciences 51  www.nature.com/scientificreports/ The propensity scores are the conditional probabilities of being assigned to the treatment group, given some vector of covariates. In our analysis, the treatment group consisted of users who were in the top one-third of their country in terms of their average daily steps (i.e., more active users). Accordingly, the control group consiseds of the bottom one-third of users (i.e., less active users). Three covariates were used for the propensity score calculations: age, BMI, and wearable data recording ratio. [Wearable data recording ratio was calculated as the total number of daily records per period of using a wearable device in days. This measure addresses a number of missing data points as well as the persistence of the users in using the wearables.] The propensity scores were calculated using logistic regression with Lasso regularization (ƛ = 0.001).
In the next step, for each unit in the treatment group, we matched the five (as a hyperparameter) most similar units from the control group based on the propensity scores by the K-Nearest Neighbor algorithm. The similarity of neighbors was calculated using Mahalanobis distance, which normalizes the distance between two points in a multivariate space. To measure the effect of activity on the sleep dimensions, we calculated the estimated average treatment effect (EATE) by the following formula 53 , where T is the treatment group, M t is the corresponding matched set from the control group, and y is the desired sleep dimension (PCA coordinates of sleep quantity or sleep quality). N t is the number of treatment units, and N M t is the size of matched control sets (in our case, N M t = 5):

Data availability
The data was collected at a large scale from Nokia wearable devices in the wild between 2014 and 2017. Consent was obtained from the users to access their anonymized records. Upon request, the corresponding authors can provide the aggregate city-level sleep metrics.  www.nature.com/scientificreports/ Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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